Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model

TL;DR

This paper proposes a novel RAEA model for entity alignment in knowledge graphs to improve cross-platform product matching, effectively utilizing attribute and relation interactions.

Contribution

Introduces RAEA, a relation-aware and attribute-aware graph attention network, for enhanced entity alignment in knowledge graphs, addressing limitations of existing methods.

Findings

RAEA outperforms 12 baselines on cross-lingual dataset DBP15K

Achieves 6.59% improvement in Hits@1 on average

Delivers competitive results on monolingual dataset DWY100K

Abstract

Product matching aims to identify identical or similar products sold on different platforms. By building knowledge graphs (KGs), the product matching problem can be converted to the Entity Alignment (EA) task, which aims to discover the equivalent entities from diverse KGs. The existing EA methods inadequately utilize both attribute triples and relation triples simultaneously, especially the interactions between them. This paper introduces a two-stage pipeline consisting of rough filter and fine filter to match products from eBay and Amazon. For fine filtering, a new framework for Entity Alignment, Relation-aware and Attribute-aware Graph Attention Networks for Entity Alignment (RAEA), is employed. RAEA focuses on the interactions between attribute triples and relation triples, where the entity representation aggregates the alignment signals from attributes and relations with Attribute-aware Entity Encoder and Relation-aware Graph Attention Networks. The experimental results indicate that the RAEA model achieves significant improvements over 12 baselines on EA task in the cross-lingual dataset DBP15K (6.59% on average Hits@1) and delivers competitive results in the monolingual dataset DWY100K. The source code for experiments on DBP15K and DWY100K is available at github (https://github.com/Mockingjay-liu/RAEA-model-for-Entity-Alignment).